Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

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Using Data Mining for Forecasting Data Management Needs

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch124 ◽

2008 ◽

pp. 2088-2104

Author(s):

Qingyu Zhang ◽

Richard S. Segall

Keyword(s):

Data Mining ◽

Data Management ◽

Forest Cover ◽

Human Lung Cancer ◽

Production Environment ◽

Data Set ◽

Biotechnology Research ◽

Use Of Data ◽

Using Data ◽

Tools And Techniques

This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of data mining with multidimensional databases for determining data management needs for the selected biotechnology data of forest cover data (63,377 rows and 54 attributes) and human lung cancer data set (12,600 rows of transcript sequences and 156 columns of gene types). The data mining is performed using four selected software of SAS® Enterprise MinerTM, Megaputer PolyAnalyst® 5.0, NeuralWare Predict®, and Bio- Discovery GeneSight®. The analysis and results will be used to enhance the intelligence capabilities of biotechnology research by improving data visualization and forecasting for organizations. The tools and techniques discussed here can be representative of those applicable in a typical manufacturing and production environment. Screen shots of each of the four selected software are presented, as are conclusions and future directions.

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Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods

Applied Sciences ◽

10.3390/app11199096 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9096

Author(s):

Idongesit Ekerete ◽

Matias Garcia-Constantino ◽

Alexandros Konios ◽

Mustafa A. Mustafa ◽

Yohanca Diaz-Skeete ◽

...

Keyword(s):

Data Mining ◽

Activity Recognition ◽

Stochastic Gradient Descent ◽

Support Vector ◽

Home Environments ◽

Use Of Data ◽

Home Based ◽

Actual Recognition ◽

Using Data ◽

Unobtrusive Sensing

This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test.

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Data Mining for Business Process Reengineering

ERP & Data Warehousing in Organizations ◽

10.4018/978-1-93177-749-0.ch009 ◽

2003 ◽

pp. 146-158

Author(s):

Ted E. Lee ◽

Robert Otondo ◽

Bonn-Oh Kim ◽

Pattarawan Prasarnphanich ◽

Ernest L. Nichols Jr.

Keyword(s):

Data Mining ◽

Criminal Justice ◽

Criminal Justice System ◽

Justice System ◽

Mining Technology ◽

Graph Theoretic ◽

Use Of Data ◽

Speed Up ◽

To Come ◽

Using Data

Transitioning from a mining to meaning perspective in organization data mining can be a crucial step in the successful application of data mining technologies. The purpose of this paper is to examine more fully the implications of that shift. The use of data mining technology was part of our cycle time study of the Poplar County Criminal Justice System (a fictitious name). In this paper we will report on the use of data mining in the Poplar County Criminal Justice System (PCCJS) study in an attempt to speed up their case handling processes. Marketing and finance researchers are more involved with “simple” (i.e., direct) relationships, whereas BPR researchers are more concerned with long chains of interacting processes. This difference appears in the tools these researchers use: marketing and finance researchers are more interested in set-theoretic problems, BPR researchers, in graph-theoretic problems. Yet data mining technologies incorporate graph-theoretic algorithms. Consequently, they should be able to support hypothesis generation in BPR activities. We were able to come up with relevant and meaningful hypotheses for BPR in the PCCJS system by using data mining technology, specifically sequential pattern analysis: “Which areas we should look into in order to speed up the case handling process?” This valuable outcome would have not been possible without data mining technology, considering the large volume of data on hand. It is hoped that this study will contribute to broadening the scope of applicability of data mining technology.

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USING DATA MINING FOR AUTOMATED SOFTWARE TESTING

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194004001737 ◽

2004 ◽

Vol 14 (04) ◽

pp. 369-393 ◽

Cited By ~ 24

Author(s):

M. LAST ◽

M. FRIEDMAN ◽

A. KANDEL

Keyword(s):

Data Mining ◽

Software Testing ◽

Data Mining Algorithm ◽

Test Automation ◽

Automated Modeling ◽

Data Mining Algorithms ◽

Use Of Data ◽

Imperfect Knowledge ◽

Using Data ◽

Automated Software

In today's software industry, the design of test cases is mostly based on human expertise, while test automation tools are limited to execution of pre-planned tests only. Evaluation of test outcomes is also associated with a considerable effort by human testers who often have imperfect knowledge of the requirements specification. Not surprisingly, this manual approach to software testing results in heavy losses to the world's economy. In this paper, we demonstrate the potential use of data mining algorithms for automated modeling of tested systems. The data mining models can be utilized for recovering system requirements, designing a minimal set of regression tests, and evaluating the correctness of software outputs. To study the feasibility of the proposed approach, we have applied a state-of-the-art data mining algorithm called Info-Fuzzy Network (IFN) to execution data of a complex mathematical package. The IFN method has shown a clear capability to identify faults in the tested program.

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Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

International Journal of Scientific Research in Science and Technology ◽

10.32628/cseit217425 ◽

2021 ◽

pp. 73-79

Author(s):

G. Ramadevi ◽

Srujitha Yeruva ◽

P. Sravanthi ◽

P. Eknath Vamsi ◽

S. Jaya Prakash

Keyword(s):

Data Mining ◽

Clustering Algorithms ◽

Medical Data ◽

Healthcare Sector ◽

Data Mining Techniques ◽

Data Mining Algorithms ◽

Use Of Data ◽

Efficiency Comparison ◽

Using Data ◽

Mining Algorithms

In a digitized world, data is growing exponentially and it is difficult to analyze the data and give the results. Data mining techniques play an important role in healthcare sector - BigData. By making use of Data mining algorithms it is possible to analyze, detect and predict the presence of disease which helps doctors to detect the disease early and in decision making. The objective of data mining techniques used is to design an automated tool that notifies the patient’s treatment history disease and medical data to doctors. Data mining techniques are very much useful in analyzing medical data to achieve meaningful and practical patterns. This project works on diabetes medical data, classification and clustering algorithms like (OPTICS, NAIVEBAYES, and BRICH) are implemented and the efficiency of the same is examined.

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Decision Support System for Diabetes Classification Using Data Mining Techniques

Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering ◽

10.4018/978-1-7998-9023-2.ch053 ◽

2021 ◽

pp. 1091-1113

Author(s):

Ahmad M. Al-Khasawneh

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Mining Algorithms ◽

Use Of Data ◽

Predictive Data Mining ◽

Severity Of The Disease ◽

Using Data

The use of data mining algorithms in health information systems has played a significant role in developing applications that help to diagnose different diseases. The type of the disease determines the selection of the algorithm, parameters to be used, and dataset pre-processing steps, etc. In this chapter, diagnosing diabetes mellitus is the target since it has gained significant attention in the last few decades due to the increased severity of the disease. Four predictive data mining approaches are being used in diagnosing diabetes. Four models were implemented to diagnose diabetes from PIMA dataset: k-nearest neighbor, support vector machine, multilayer perceptron neural network, and naive Bayesian network. Giving the highest classification accuracy, support vector machine technique outperformed the others with a value of 78.83%.

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Using data mining to process business data

Problems of Innovation and Investment Development ◽

10.33813/2224-1213.23.2020.8 ◽

2020 ◽

pp. 71-77

Author(s):

Chubukova ◽

Ponomarenko ◽

Nedbailo

Keyword(s):

Machine Learning ◽

Data Mining ◽

Programming Languages ◽

Complex Analysis ◽

Primary Data ◽

Business Analytics ◽

Use Of Data ◽

The Subject ◽

Mining Methods ◽

Using Data

The subject of the research is the approach to the possibility of applying data mining methods in the framework of business analytics in order to optimize the adoption of management decisions by the company.The purpose of writing this article is to study of data mining methods features use of primary data, which act as a valuable resource of the company, which can be used to ensure competitive- ness in a particular market. Methodology. The research methodology is system- structural and comparative analyzes (to study the approaches of data mining data for the complex analysis of first data); monograph (studying the preconditions for the growth of data mining companies’ use in the process of data research); eco- nomic analysis (when assessing the feasibility of using machine learning methods to ensure the goals of business intelligence). The scientific novelty consists the peculiarities of data mining application as one of the directions of business analyt- ics are determined, which makes it possible to determine implicit relationships between known factor and result characteristics on the basis of primary data. The main directions of data manipulation are revealed: classification and forecasting, as well as correlation-regression analysis. The importance of using the basic meth- ods of statistical analysis in the process of studying primary data is proved. The specifics of the use of neural networks as one of the most important methods of machine learning are given. Conclusions. The use of data mining allows companies to increase the efficiency of the use of available data and optimize development strategies accordingly. The presence of a large number of machine learning meth- ods and statistical approaches expands the possibilities of comprehensive data analysis. Innovative technologies and specialized programming languages play an important role in this case.

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Decision Support System for Diabetes Classification Using Data Mining Techniques

Advances in Healthcare Information Systems and Administration - Handbook of Research on Emerging Perspectives on Healthcare Information Systems and Informatics ◽

10.4018/978-1-5225-5460-8.ch012 ◽

2018 ◽

pp. 281-303

Author(s):

Ahmad M. Al-Khasawneh

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Mining Algorithms ◽

Use Of Data ◽

Predictive Data Mining ◽

Severity Of The Disease ◽

Using Data

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Application of Data Mining Method Using Association Rules Apriori To Shopping Cart Analysis On Sale Transactions (Case Study Alfamidi Burnt Stone)

Journal Of Computer Networks, Architecture and High Performance Computing ◽

10.47709/cnapc.v2i2.425 ◽

2020 ◽

Vol 2 (2) ◽

pp. 222-226

Author(s):

Vanessa Siregar ◽

Paska Marto Hasugian

Keyword(s):

Data Mining ◽

Knowledge Discovery In Databases ◽

Data Sets ◽

Cart Analysis ◽

Data Mining Algorithms ◽

Use Of Data ◽

Large Size ◽

Using Data ◽

Mining Algorithms

Also Often data mining is called knowledge discovery in databases (KDD), ie activities include the collection, historical use of data to find regularities, patterns or relationships in data sets with a large size. The company may be interested to know if some groups consistently goods items purchased together. This study analyzes the transaction of data information retrieval from the sale of skin care and hair care using data mining algorithms priori Alfamidi Burnt Stones with the highest support value is 8% and the highest value is 5% confidance

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Application to Bankruptcy Prediction in Banks

Handbook of Research on Machine Learning Applications and Trends ◽

10.4018/978-1-60566-766-9.ch020 ◽

2010 ◽

pp. 427-439

Author(s):

Joaquín Ordieres-Meré ◽

Ana González-Marcos ◽

Manuel Castejón-Limas ◽

Francisco J. Martínez-de-Pisón

Keyword(s):

Data Mining ◽

High Volume ◽

Bankruptcy Prediction ◽

Data Mining Techniques ◽

Use Of Data ◽

High Volume Production ◽

Data Driven Approach ◽

Hidden Knowledge ◽

Volume Production ◽

Using Data

This chapter reports five experiences in successfully applying different data mining techniques in a hotdip galvanizing line. Engineers working in steelmaking have traditionally built mathematical models either for their processes or products using classical techniques. Their need to continuously cut costs down while increasing productivity and product quality is now pushing the industry into using data mining techniques so as to gain deeper insights into their manufacturing processes. The authors’ work was aimed at extracting hidden knowledge from massive data bases in order to improve the existing control systems. The results obtained, though small at first glance, lead to huge savings at such high volume production environment. The effective solutions provided by the use of data mining techniques along these projects encourages the authors to continue applying this data driven approach to frequent hard-to-solve problems in the steel industry.

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